Agent-Based Modeling of Internal Migration

基于代理的内部迁移建模

• 批准号: 8931778
• 负责人: LINGXIN HAO
• 金额: $ 19.46万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-26 至 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8931778
• 关键词: Accounting; Age; Algorithms; Area; Benchmarking; Capital; Censuses; China; Chinese People; Complex; Computer Simulation; Contrast Media; Country; Data; Databases; Decision Making; Destinations; Development; Disadvantaged; Economic Conditions; Economics; Education; Employment; Environment; Etiology; Face; Family member; Feedback; Gender; Health; Health Care Costs; Home environment; Household; Immigration; Individual; Industry; Inequality; Internal Migrations; International; Intervention; Investments; Labor Forces; Literature; Marketing; Methods; Mexico; Modeling; Network-based; Nomads; Occupations; Outcome; Pattern; Personal Satisfaction; Policies; Population; Population Distributions; Population Growth; Population Research; Probability; Process; Production; Property; Province; Public Health; Publishing; Recording of previous events; Research; Rights; Rural; Rural Developments; Rural Spatial Distributions; Stress; Surveys; System; Techniques; Testing; Theoretical model; United States; Urbanization; Validation; Wages; base; cluster computing; experience; improved; innovation; insight; migration; outcome forecast; preference; pressure; public health relevance; research study; rural-urban migration; social; statistics; theories

DESCRIPTION (provided by applicant): Agent-based Modeling of Rural-urban Migration in China We propose to develop a large-scale agent-based model of rural-to-urban migration in China. Large-scale migrations, both within countries and across countries, are transforming the world's population. Agent-based modeling (ABM) is a promising and underutilized means for understanding the implications of these complex migrations for population distribution and growth because it can include feedback loops, allow actor-place interactions, and reveal unexpected, emergent properties of the complex system of migration. Recent developments in ABM, notably algorithms for the efficient use of powerful computing clusters to estimate models with billions of actors (Parker and Epstein 2011), make this technique especially promising for modeling large, complex spatial processes. Thus, we would argue that the development of ABM to study large-scale migrations is an innovation that is of high scientific priority for population research. China is an excellent choice for the development of the first such model because it has, since the mid-1980s, undergone the largest single migration in the world's history, transferring some 230 million rural people by 2011, accounting for 17.4% of China's total population (Chinese National Bureau of Statistics 2012). About half of China's rural migration is from the rural inner provinces to the urbanized coast. Rural migrants are drawn to coastal areas primarily because of the high employment prospects in export-oriented industries and migrant networks (Hao et al. 2013). This monumental population shift has global economic, demographic, health, and political ramifications. While its importance is well documented, little has been done to identify and test the basic underlying causal mechanisms. The scale and duration of China's mass migration present a unique opportunity to gain valuable insights into this important historical phenomenon. The project will (1) explore, develop, and validate the conceptual model by reformulating and improving existing theories to apply to China's rural-urban migration; (2) develop a large-scale agent-based model of rural-urban migration in China, using empirically calibrated initial conditions, ex ante structural parameters, and "open" parameters to be experimented ex post, and validate the simulated stylized patterns against the reality; (3) test the explanatory power of each of the component theories integrated in our base-case model; (4) explore the dynamic outcomes of the base-case model under varying policies, economic conditions, individual preferences, and initial population conditions, and (5) find general principles of mass migration and to produce and publish our methods and results so that ABM can be applied to other large, complex migration flows.

描述(申请人提供)：中国的基于主体的农村向城市迁移模型我们建议在中国建立一个大规模的基于主体的农村向城市迁移模型。国家内部和国家之间的大规模移徙正在改变世界人口。基于主体的建模(ABM)是理解这些复杂迁移对人口分布和增长的影响的一种有前途但未得到充分利用的手段，因为它可以包括反馈循环，允许参与者-地点交互，并揭示复杂迁移系统的意外和紧急性质。ABM的最新发展，特别是高效使用强大的计算集群来估计具有数十亿个参与者的模型的算法(Parker和Epstein 2011)，使这项技术在建模大型、复杂的空间过程方面特别有希望。因此，我们认为，发展ABM来研究大规模迁徙是一项对人口具有高度科学优先权的创新 研究。中国是发展第一个这样的模式的绝佳选择，因为自20世纪80年代中期以来，中国经历了世界历史上最大规模的一次移民，到2011年转移了约2.3亿农村人口，占中国总人口的17.4%(中国国家统计局，2012年)。中国的农村移民中，大约有一半是从内陆农村到城市化的沿海地区。农村移民被吸引到沿海地区，主要是因为出口导向型行业和移民网络的就业前景很高(Hao等人)。2013年)。这一巨大的人口迁移对全球经济、人口、健康和政治都有影响。虽然它的重要性被很好地记录下来，但在识别和测试基本的潜在因果机制方面做的工作很少。中国大规模迁徙的规模和持续时间为了解这一重要历史现象提供了一个独特的机会。该项目将(1)通过重塑和改进现有理论来探索、开发和验证概念模型，以应用于中国的城乡迁移；(2)开发基于主体的大规模中国城乡迁移模型，使用经验校准的初始条件、事前结构参数和“开放”参数进行事后实验，并验证模拟的风格化模式与实际情况；(3)检验我们的基本情况模型中集成的各个组成部分理论的解释能力；(4)探索基本情况模型在不同政策、经济条件、个人偏好和初始人口条件下的动态结果，以及(5)发现大规模移民的一般原理，并产生和公布我们的方法和结果，以便ABM可以应用于其他大型、复杂的移民流动。